Comparison of Optimality Criteria of Reduced Models for Response Surface Designs with Restricted Randomization

Abstract

In this work, D−, G−, and A− efficiencies and the scaled average prediction variance, IV criterion, are computed and compared for second-order split-plot central composite design. These design optimality criteria are evaluated across the set of reduced split-plot central composite design models for three design variables under various ratios of the variance components (or degrees of correlation d). It was observed that D, A, G, and IV for these models strongly depend on the values of d; they are robust to changes in the interaction terms and vary dramatically with the number of, and changes in the squared terms.